\section{Energy-related variables}
In the second part of our study, we employ our historical database of daily weather regimes to support a more refined quantification of correlated energy variables \parencite{grams, van_der_Wiel_2019, VANDERWIEL2019261, Bloomfield_2016}.\\
Our focus is primarily on the EU-7 countries (France, Germany, United Kingdom, Italy, Spain, Belgium, Netherlands), mostly concerning Power demand, Wind/Solar production, and Hydro generation in the North pool (Scandinavia). Together, they account for almost the entirety of the power-trading operations in Europe, and our intent is to model more robustly the energy-related variables at the base of their strategies. To do so, as mentioned in Section \ref{sec:meteo_data}, we need to plug in additional weather variables, which we know they tend to present very informative correlations with the energy counterparts. As an example, Figure \ref{fig:load_temp_FR} presents two very definite trends relating Temperature and Power Demand, and Wind Generation and Price, respectively.
\begin{figure}[!htbp]
    \centering
    \begin{subfigure}{.49\textwidth}
    \centering
    \includegraphics[width=.9\textwidth]{imgs/temperature_vs_load_bdays.png}
    \caption{Example of hockey-stick correlation between an additional weather variable (Temperature) and an energy-related variable (Power Demand). The correlation is strongly negative: at low temperatures heating systems drives most of the Power Load; at high temperatures, cooling systems are advocated for the increase in the correlation}
    \end{subfigure}
    \hfill
    \begin{subfigure}{.49\textwidth}
    \centering
    \includegraphics[width=.9\textwidth]{imgs/Total Wind Obs_vs_Price_holidays.png}
    \caption{Example of relationship between the Total Wind Generation (GW) and Price (€/Mwh). It is interesting to notice an overall negative correlation, due to the production satisfying the market demand. Further, when the production overwhelms it (over-production) it could happen that the price goes negative (energy providers are asked to buy the produced energy, and stock it)}
    \end{subfigure}
    \caption{Examples of relationships across weather and energy-related variables}
    \label{fig:load_temp_FR}
\end{figure}
There is some evidence that weather regimes could potentially explain much of the underlying variability affecting energy markets. Especially for NAOs \parencite{10.31223/osf.io/8sntx, 10.1175/JAMC-D-12-0257.1}, which tend to be the predominant regimes during winter, a lot of the volatility of energy variables could be better addressed and quantified. Hence, in our second part we seek to answer the following questions:
\begin{itemize}
    \item \textit{Inter-regimes quantification}: how different regimes lead to different energetic configurations?
    \item \textit{Intra-regimes quantification}: which effects mostly characterize a particular regime?
    \item \textit{Inter-counties quantification}: how the effects of a persistence of a regime vary with geographical position?
    \item \textit{Intra-countries quantification}: how each country is subject to internal variability?
    \item \textit{Extreme events}: which regimes most likely drive extreme events? How do they differ from one country to the other?
\end{itemize}

